This series of files compile all analyses done during Chapter 1 for the regional campaign of 2016:
All analyses have been done with PRIMER-e 6 and R 3.6.2.
Click on the table of contents in the left margin to assess a specific analysis.
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We used data from subtidal ecosystems (see metadata files for more information). Only stations that have been sampled both for abiotic parameters and benthic species were included.
Selected variables for the analyses:
- Depth of the station: depth (only for ANCOVAs)
- Percentage of organic matter: om
- Percentage of gravel: gravel
- Percentage of sand: sand
- Percentage of silt: silt
- Percentage of clay: clay
- Concentration of arsenic: arsenic
- Concentration of cadmium: cadmium
- Concentration of chromium: chromium
- Concentration of copper: copper
- Concentration of iron: iron
- Concentration of manganese: manganese
- Concentration of mercury: mercury
- Concentration of lead: lead
- Concentration of zinc: zinc
- Species richness: S
- Abundance of total individuals: N
- Shannon index: H
- Piélou evenness: J
Abundances of Mesodesma arctatum (Marc) and Cistenides granulata (Cgra) were also considered (see IndVal and SIMPER results).
As data is missing for metal concentrations outside BSI, two Designs have been used:
- Design 1: stations at BSI, CPC, BDA, MR with habitat parameters
- Design 2: stations at BSI with heavy metal concentrations.
1. Data manipulation
For the following analyses, independant variables are habitat parameters and heavy metal concentrations, dependant variables are diversity indices.
1.1. Identification of outliers
To identify stations that are not consistent with the others, we used the multivariate Cook’s Distance (CD) on the uncorrelated variables. A significative threshold of 4 times the mean of CD has been established.
Design 1

Based on Cook’s Distance, we identified stations 60, 72, 80 and 96 as general outliers. They have been deleted for the following analyses of Design 1.
Design 2

Based on Cook’s Distance, we identified stations 108 and 110 as general outliers. They have been deleted for the following analyses of Design 2.
1.2. Correlations between parameters
Correlations have been calculated with Spearman’s rank coefficient.
Design 1
Correlation coefficients between habitat parameters (Design 1)
| om |
1 |
-0.068 |
-0.807 |
0.714 |
0.706 |
| gravel |
-0.068 |
1 |
-0.192 |
-0.37 |
-0.329 |
| sand |
-0.807 |
-0.192 |
1 |
-0.772 |
-0.768 |
| silt |
0.714 |
-0.37 |
-0.772 |
1 |
0.973 |
| clay |
0.706 |
-0.329 |
-0.768 |
0.973 |
1 |

According to these results, the following variables are highly correlated (\(|\rho|\) > 0.80) so they have been considered together in the regressions of Design 1:
- silt and clay (clay deleted)
We decided to keep sand, even if it is correlated with om, to stay consistant with the 2014 campaign.

Design 2
Correlation coefficients between heavy metals concentrations (Design 2)
| arsenic |
1 |
0.492 |
0.736 |
0.876 |
0.773 |
0.399 |
0.646 |
0.816 |
0.903 |
| cadmium |
0.492 |
1 |
0.757 |
0.41 |
0.766 |
0.881 |
0.154 |
0.708 |
0.663 |
| chromium |
0.736 |
0.757 |
1 |
0.712 |
0.825 |
0.767 |
0.463 |
0.85 |
0.879 |
| copper |
0.876 |
0.41 |
0.712 |
1 |
0.633 |
0.38 |
0.572 |
0.829 |
0.89 |
| iron |
0.773 |
0.766 |
0.825 |
0.633 |
1 |
0.755 |
0.429 |
0.745 |
0.842 |
| manganese |
0.399 |
0.881 |
0.767 |
0.38 |
0.755 |
1 |
0.105 |
0.584 |
0.628 |
| mercury |
0.646 |
0.154 |
0.463 |
0.572 |
0.429 |
0.105 |
1 |
0.627 |
0.545 |
| lead |
0.816 |
0.708 |
0.85 |
0.829 |
0.745 |
0.584 |
0.627 |
1 |
0.898 |
| zinc |
0.903 |
0.663 |
0.879 |
0.89 |
0.842 |
0.628 |
0.545 |
0.898 |
1 |

According to these results, the following variables are highly correlated (\(|\rho|\) > 0.80) so they have been considered together in the regressions of Design 2:
- cadmium and manganese (manganese deleted)
- copper, lead and zinc (copper and zinc deleted)
We decided to keep arsenic, even though it is correlated with the copper/lead/zinc group, to stay consistant with the 2014 campaign.

2. Permutational Analyses of Covariance
Results of univariate PermANCOVAs on parameters and multivariate PermANCOVA on the whole benthic community with depth as covariate are presented in the table below. Variables were normalized and abundances were (log+1) transformed.
| om |
|
S |
S |
{CPC BDA MR} |
| gravel |
|
|
|
All regions in the same group |
| sand |
|
|
S |
All regions in the same group |
| silt |
S |
|
S |
{BSI CPC BDA}, {BDA MR} |
| clay |
|
|
|
{BSI BDA MR}, {CPC MR} |
| S (1 mm) |
|
|
S |
{BSI CPC MR}, {CPC BDA MR} |
| N (1 mm) |
|
|
|
All regions in the same group |
| H (1 mm) |
|
s~ |
S |
{CPC BDA MR}, {BSI MR} |
| J (1 mm) |
|
|
|
{BSI CPC MR}, {CPC BDA MR} |
| ALL SPECIES (1 mm) |
|
S |
S |
|
3. Similarity and characteristic species
Let’s have a look at the \(\beta\) diversity within our conditions and sites.
Results of the PERMDISP routine are shown below (mean and SE of the deviation from centroid for each group, i.e. multivariate dispersion), along with the mean Bray-Curtis dissimilarity for each group. Abundances were (log+1) transformed and PRIMER was used to do the PERMDISP.
Mean within-group Bray-Curtis dissimilarity for each condition or site
| HI |
64.6 |
0.83 |
0.917 |
| R |
61.9 |
1.14 |
0.878 |
| BSI |
62.9 |
1.18 |
0.903 |
| CPC |
60.2 |
2.25 |
0.87 |
| BDA |
61.1 |
1.93 |
0.882 |
| MR |
58.2 |
2.12 |
0.835 |
No significative relationships were found for either factor by the PERMDISP (p = 0.069) or the pairwise tests.
The following analyses allowed to detect species as characteristic of each condition. We used results from PRIMER to justify further their choice.
## cluster indicator_value probability
## cistenides_granulata 1 0.2836 0.018
## macoma_calcarea 1 0.2326 0.002
## ennucula_tenuis 1 0.1860 0.018
## eudorellopsis_integra 1 0.1395 0.029
## mesodesma_arctatum 2 0.2342 0.007
## harmothoe_imbricata 2 0.1975 0.010
## glycera_alba 2 0.1212 0.039
## psammonyx_nobilis 2 0.1212 0.029
##
## Sum of probabilities = 50.871
##
## Sum of Indicator Values = 5.89
##
## Sum of Significant Indicator Values = 1.52
##
## Number of Significant Indicators = 8
##
## Significant Indicator Distribution
##
## 1 2
## 4 4
SIMPER results (mean Bray-Curtis between-group dissimilarity: 0.926)
| echinarachnius_parma |
0.0984 |
0.136 |
0.721 |
0.689 |
0.42 |
0.106 |
| mesodesma_arctatum |
0.07 |
0.129 |
0.542 |
0.605 |
0.0995 |
0.182 |
| cistenides_granulata |
0.0609 |
0.0948 |
0.643 |
0.176 |
0.565 |
0.248 |
| strongylocentrotus_sp |
0.0427 |
0.0758 |
0.563 |
0.27 |
0.249 |
0.294 |
| nephtys_caeca |
0.0425 |
0.0556 |
0.764 |
0.359 |
0.23 |
0.34 |
| limecola_balthica |
0.0313 |
0.0578 |
0.542 |
0.234 |
0.18 |
0.373 |
| scoloplos_armiger |
0.0295 |
0.065 |
0.453 |
0.14 |
0.256 |
0.405 |
| macoma_calcarea |
0.0274 |
0.0569 |
0.482 |
0 |
0.312 |
0.435 |
| harmothoe_imbricata |
0.0257 |
0.0583 |
0.44 |
0.217 |
0.0161 |
0.462 |
| amphipholis_squamata |
0.0238 |
0.0611 |
0.389 |
0.042 |
0.241 |
0.488 |
| protomedeia_grandimana |
0.0228 |
0.0538 |
0.424 |
0.183 |
0.169 |
0.513 |
| psammonyx_nobilis |
0.0189 |
0.0592 |
0.32 |
0.185 |
0 |
0.533 |
| thyasira_sp |
0.0186 |
0.0469 |
0.397 |
0.021 |
0.241 |
0.553 |
| ennucula_tenuis |
0.0185 |
0.0422 |
0.438 |
0 |
0.241 |
0.573 |
| mya_arenaria |
0.0174 |
0.034 |
0.513 |
0.063 |
0.168 |
0.592 |
| ciliatocardium_ciliatum |
0.014 |
0.045 |
0.312 |
0.0908 |
0.0766 |
0.607 |
| goniada_maculata |
0.0139 |
0.0354 |
0.391 |
0.021 |
0.173 |
0.622 |
| glycera_dibranchiata |
0.0134 |
0.043 |
0.31 |
0.021 |
0.0806 |
0.637 |
| glycera_alba |
0.0128 |
0.0408 |
0.313 |
0.172 |
0 |
0.65 |
| ameritella_agilis |
0.0117 |
0.0491 |
0.238 |
0 |
0.131 |
0.663 |
| astarte_undata |
0.0117 |
0.0388 |
0.301 |
0.142 |
0 |
0.676 |
| astarte_subaequilatera |
0.0106 |
0.0363 |
0.293 |
0.134 |
0 |
0.687 |
| nucula_proxima |
0.00992 |
0.0349 |
0.284 |
0 |
0.112 |
0.698 |
| pygospio_elegans |
0.00989 |
0.0449 |
0.22 |
0.137 |
0.0161 |
0.708 |
| ophelia_limacina |
0.00977 |
0.0299 |
0.327 |
0.042 |
0.0578 |
0.719 |
| diastylis_sculpta |
0.00966 |
0.0405 |
0.238 |
0.0488 |
0.0322 |
0.729 |
| eudorellopsis_integra |
0.00955 |
0.0267 |
0.358 |
0 |
0.153 |
0.74 |
| ampharetidae_spp |
0.00948 |
0.0277 |
0.342 |
0.0753 |
0.0535 |
0.75 |
| yoldia_myalis |
0.00913 |
0.0285 |
0.321 |
0.0543 |
0.0484 |
0.76 |
| nephtys_bucera |
0.00905 |
0.0256 |
0.354 |
0.063 |
0.0322 |
0.77 |
| ampeliscidae_spp |
0.00898 |
0.0253 |
0.354 |
0.063 |
0.0511 |
0.779 |
| pontoporeia_femorata |
0.00877 |
0.0404 |
0.217 |
0 |
0.132 |
0.789 |
| bipalponephtys_neotena |
0.00836 |
0.037 |
0.226 |
0 |
0.106 |
0.798 |
| maldanidae_spp |
0.00825 |
0.0272 |
0.303 |
0.0908 |
0.0322 |
0.807 |
| pagurus_pubescens |
0.00766 |
0.0231 |
0.331 |
0.0753 |
0.0161 |
0.815 |
| polynoidae_spp |
0.00756 |
0.0217 |
0.349 |
0.021 |
0.0952 |
0.823 |
| ampharete_oculata |
0.00725 |
0.0439 |
0.165 |
0.0666 |
0 |
0.831 |
| phyllodoce_mucosa |
0.00643 |
0.0241 |
0.267 |
0 |
0.106 |
0.838 |
| phyllodocidae_spp |
0.00629 |
0.0211 |
0.298 |
0.021 |
0.0484 |
0.845 |
| phoxocephalus_holbolli |
0.00621 |
0.0329 |
0.189 |
0 |
0.0827 |
0.851 |
| testudinalia_testudinalis |
0.00576 |
0.026 |
0.222 |
0.08 |
0 |
0.858 |
| harpinia_propinqua |
0.00547 |
0.0253 |
0.216 |
0.0753 |
0.0161 |
0.864 |
| quasimelita_formosa |
0.00486 |
0.0192 |
0.253 |
0 |
0.0739 |
0.869 |
| nephtys_ciliata |
0.00455 |
0.0213 |
0.214 |
0 |
0.0645 |
0.874 |
| platyhelminthes |
0.00429 |
0.0164 |
0.262 |
0 |
0.0484 |
0.878 |
| lacuna_vincta |
0.00427 |
0.0233 |
0.184 |
0 |
0.0417 |
0.883 |
| cancer_irroratus |
0.00405 |
0.0143 |
0.283 |
0.042 |
0.0161 |
0.887 |
| nephtys_incisa |
0.00399 |
0.0185 |
0.216 |
0.021 |
0.0161 |
0.892 |
| arrhoges_occidentalis |
0.00398 |
0.0167 |
0.239 |
0.0543 |
0 |
0.896 |
4. Univariate regressions
We used linear models for the all regressions on diversity indices. Outliers and correlated variables were removed from these analyses.
4.1. Simple regressions
These analyses have been do to explore the relationships between variables. As it is a huge number of results to interpret, only multiple regressions will be included in the article (see below).
Design 1
Adjusted R-squared of simple regressions for Design 1
| S |
0.09824 |
0.06215 |
0.0708 |
0.1258 |
| N |
0.01242 |
0.01491 |
0.03477 |
0.03467 |
| H |
0.09519 |
0.03329 |
0.06053 |
0.1134 |
| J |
0.004809 |
-0.0122 |
0.01178 |
0.01984 |
p-values of simple regressions for Design 1
| S |
0.00425 |
0.01962 |
0.01359 |
0.001309 |
| N |
0.1732 |
0.1542 |
0.06343 |
0.06371 |
| H |
0.004839 |
0.06765 |
0.02101 |
0.002229 |
| J |
0.2504 |
0.7054 |
0.1785 |
0.123 |
Design 2
Adjusted R-squared of simple regressions for Design 2
| S |
-0.01268 |
-0.04896 |
-0.03331 |
-0.04823 |
-0.047 |
0.06622 |
| N |
0.008407 |
-0.04909 |
-0.03615 |
-0.04682 |
-0.04877 |
0.03425 |
| H |
-0.01205 |
-0.03027 |
-0.001362 |
-0.02749 |
-0.02325 |
0.102 |
| J |
-0.04952 |
-0.01768 |
-0.0304 |
-0.03285 |
-0.03656 |
-0.04851 |
p-values of simple regressions for Design 2
| S |
0.4008 |
0.8897 |
0.5762 |
0.8559 |
0.8132 |
0.1303 |
| N |
0.2907 |
0.8964 |
0.6107 |
0.8078 |
0.8796 |
0.2014 |
| H |
0.3967 |
0.543 |
0.3361 |
0.5155 |
0.478 |
0.08065 |
| J |
0.9251 |
0.4348 |
0.5443 |
0.5708 |
0.6162 |
0.8677 |
Furthermore, depth has been shown important for several parameters in the ANCOVAs, so here are the corresponding scatterplots.

4.2. Multiple regressions
This section presents analyses done (i) to determine which model (Design 1, Design 2) decribes the best the parameters and (ii) which variables are the most important to explain the parameters.
4.2.1. Best model selection
This step was not used here as both models were needed.
4.2.2. Significative variables selection
We identified which variables were selected after an AIC procedure to predict the best the parameters. Results of the variable selection, according to AIC, are shown on the tables below:
- for the model of Design 1
| om |
|
|
|
|
| gravel |
|
- |
+ |
|
| sand |
+ |
- |
+ |
|
| silt/clay |
+ |
- |
+ |
+ |
| Adjusted \(R^{2}\) |
0.17 |
0.1 |
0.18 |
0.02 |
- for the model of Design 2
| arsenic |
|
|
|
|
| cadmium/manganese |
|
|
|
|
| chromium |
- |
- |
- |
|
| iron |
|
|
|
|
| mercury |
|
|
|
|
| lead/copper/zinc |
+ |
+ |
+ |
|
| Adjusted \(R^{2}\) |
0.29 |
0.16 |
0.21 |
0 |
Details of the regressions, with diagnostics and cross-validation, are summarized below.
Design 1
Species richness
## FULL MODEL
## Adjusted R2 is: 0.15
Fitting linear model: S ~ om + gravel + sand + silt
| (Intercept) |
-5.815 |
7.696 |
-0.7556 |
0.4526 |
|
| om |
-0.1275 |
0.8173 |
-0.1561 |
0.8765 |
|
| gravel |
3.618 |
8.918 |
0.4057 |
0.6863 |
|
| sand |
10.11 |
7.78 |
1.3 |
0.1982 |
|
| silt |
15.05 |
9.997 |
1.505 |
0.137 |
|
## FULL MODEL
## Diagnostics: cf plots
## RMSE from cross-validation: 2.60029
Variance Inflation Factors
| VIF |
2.01 |
2.35 |
8.23 |
9.4 |

## REDUCED MODEL
## Adjusted R2 is: 0.17
Fitting linear model: S ~ sand + silt
| (Intercept) |
-2.998 |
3.167 |
-0.9466 |
0.3471 |
|
| sand |
7.299 |
3.315 |
2.202 |
0.03102 |
* |
| silt |
11.48 |
3.727 |
3.081 |
0.002963 |
* * |
## REDUCED MODEL
## Diagnostics: cf plots
## RMSE from cross-validation: 2.515746
Variance Inflation Factors
| VIF |
3.55 |
3.55 |

Total abundance
## FULL MODEL
## Adjusted R2 is: 0.1
Fitting linear model: N ~ om + gravel + sand + silt
| (Intercept) |
215.1 |
74.6 |
2.883 |
0.005295 |
* * |
| om |
8.721 |
7.923 |
1.101 |
0.2749 |
|
| gravel |
-244.9 |
86.46 |
-2.833 |
0.006085 |
* * |
| sand |
-199.8 |
75.42 |
-2.649 |
0.01006 |
* |
| silt |
-231.5 |
96.92 |
-2.389 |
0.01974 |
* |
## FULL MODEL
## Diagnostics: cf plots
## RMSE from cross-validation: 30.52945
Variance Inflation Factors
| VIF |
2.01 |
2.35 |
8.23 |
9.4 |

## REDUCED MODEL
## Adjusted R2 is: 0.1
Fitting linear model: N ~ gravel + sand + silt
| (Intercept) |
176.7 |
66.07 |
2.674 |
0.009366 |
* * |
| gravel |
-201.2 |
76.9 |
-2.616 |
0.01094 |
* |
| sand |
-159.3 |
65.94 |
-2.416 |
0.0184 |
* |
| silt |
-166 |
76.63 |
-2.166 |
0.03379 |
* |
## REDUCED MODEL
## Diagnostics: cf plots
## RMSE from cross-validation: 29.91383
Variance Inflation Factors
| VIF |
2.09 |
7.18 |
7.42 |

Shannon index
## FULL MODEL
## Adjusted R2 is: 0.17
Fitting linear model: H ~ om + gravel + sand + silt
| (Intercept) |
-2.954 |
1.61 |
-1.834 |
0.07105 |
|
| om |
-0.1016 |
0.171 |
-0.5938 |
0.5546 |
|
| gravel |
3.041 |
1.866 |
1.63 |
0.1079 |
|
| sand |
3.949 |
1.628 |
2.426 |
0.01798 |
* |
| silt |
5.424 |
2.092 |
2.593 |
0.01168 |
* |
## FULL MODEL
## Diagnostics: cf plots
## RMSE from cross-validation: 0.5255413
Variance Inflation Factors
| VIF |
2.01 |
2.35 |
8.23 |
9.4 |

## REDUCED MODEL
## Adjusted R2 is: 0.18
Fitting linear model: H ~ gravel + sand + silt
| (Intercept) |
-2.507 |
1.417 |
-1.769 |
0.08133 |
|
| gravel |
2.532 |
1.649 |
1.535 |
0.1295 |
|
| sand |
3.477 |
1.414 |
2.459 |
0.01649 |
* |
| silt |
4.662 |
1.644 |
2.836 |
0.006011 |
* * |
## REDUCED MODEL
## Diagnostics: cf plots
## RMSE from cross-validation: 0.5269319
Variance Inflation Factors
| VIF |
2.09 |
7.18 |
7.42 |

Piélou’s evenness
## FULL MODEL
## Adjusted R2 is: 0
Fitting linear model: J ~ om + gravel + sand + silt
| (Intercept) |
-0.3581 |
0.8682 |
-0.4124 |
0.6814 |
|
| om |
-0.07058 |
0.0922 |
-0.7655 |
0.4467 |
|
| gravel |
1.11 |
1.006 |
1.104 |
0.2737 |
|
| sand |
1.07 |
0.8778 |
1.219 |
0.2273 |
|
| silt |
1.569 |
1.128 |
1.391 |
0.1688 |
|
## FULL MODEL
## Diagnostics: cf plots
## RMSE from cross-validation: 0.3016641
Variance Inflation Factors
| VIF |
2.01 |
2.35 |
8.23 |
9.4 |

## REDUCED MODEL
## Adjusted R2 is: 0.02
Fitting linear model: J ~ silt
| (Intercept) |
0.6944 |
0.0404 |
17.19 |
8e-27 |
* * * |
| silt |
0.1853 |
0.1187 |
1.561 |
0.123 |
|
## REDUCED MODEL
## Diagnostics: cf plots
## RMSE from cross-validation: 0.2855537
Variance Inflation Factors
| VIF |
1 |

Design 2
Species richness
## FULL MODEL
## Adjusted R2 is: 0.23
Fitting linear model: S ~ arsenic + cadmium + chromium + iron + mercury + lead
| (Intercept) |
8.292 |
2.311 |
3.587 |
0.002696 |
* * |
| arsenic |
-0.06374 |
0.2517 |
-0.2532 |
0.8035 |
|
| cadmium |
-4.257 |
22.55 |
-0.1888 |
0.8528 |
|
| chromium |
-0.1487 |
0.1001 |
-1.486 |
0.1581 |
|
| iron |
-8.05e-05 |
0.0001006 |
-0.8002 |
0.4361 |
|
| mercury |
-52.39 |
38.5 |
-1.361 |
0.1937 |
|
| lead |
2.059 |
0.6635 |
3.103 |
0.007277 |
* * |
## FULL MODEL
## Diagnostics: cf plots
## RMSE from cross-validation: 3.108151
Variance Inflation Factors
| VIF |
2.19 |
1.86 |
3.63 |
2.85 |
1.21 |
3.25 |

## REDUCED MODEL
## Adjusted R2 is: 0.29
Fitting linear model: S ~ chromium + lead
| (Intercept) |
6.618 |
1.478 |
4.479 |
0.0002574 |
* * * |
| chromium |
-0.1919 |
0.07173 |
-2.675 |
0.01499 |
* |
| lead |
1.677 |
0.532 |
3.153 |
0.005237 |
* * |
## REDUCED MODEL
## Diagnostics: cf plots
## RMSE from cross-validation: 2.508174
Variance Inflation Factors
| VIF |
2.7 |
2.7 |

Total abundance
## FULL MODEL
## Adjusted R2 is: 0.04
Fitting linear model: N ~ arsenic + cadmium + chromium + iron + mercury + lead
| (Intercept) |
34.8 |
15.49 |
2.247 |
0.04011 |
* |
| arsenic |
0.7435 |
1.686 |
0.4409 |
0.6656 |
|
| cadmium |
19.5 |
151.1 |
0.129 |
0.8991 |
|
| chromium |
-0.7334 |
0.6704 |
-1.094 |
0.2912 |
|
| iron |
-0.0005478 |
0.000674 |
-0.8128 |
0.429 |
|
| mercury |
-236.2 |
258 |
-0.9155 |
0.3744 |
|
| lead |
8.931 |
4.446 |
2.009 |
0.06288 |
|
## FULL MODEL
## Diagnostics: cf plots
## RMSE from cross-validation: 24.0044
Variance Inflation Factors
| VIF |
2.19 |
1.86 |
3.63 |
2.85 |
1.21 |
3.25 |

## REDUCED MODEL
## Adjusted R2 is: 0.16
Fitting linear model: N ~ chromium + lead
| (Intercept) |
24.22 |
9.576 |
2.529 |
0.02046 |
* |
| chromium |
-0.9287 |
0.4648 |
-1.998 |
0.06024 |
|
| lead |
8.207 |
3.447 |
2.381 |
0.0279 |
* |
## REDUCED MODEL
## Diagnostics: cf plots
## RMSE from cross-validation: 16.71375
Variance Inflation Factors
| VIF |
2.7 |
2.7 |

Shannon index
## FULL MODEL
## Adjusted R2 is: 0.06
Fitting linear model: H ~ arsenic + cadmium + chromium + iron + mercury + lead
| (Intercept) |
1.35 |
0.431 |
3.133 |
0.006841 |
* * |
| arsenic |
-0.02749 |
0.04693 |
-0.5857 |
0.5668 |
|
| cadmium |
-0.5051 |
4.206 |
-0.1201 |
0.906 |
|
| chromium |
-0.02101 |
0.01866 |
-1.126 |
0.2778 |
|
| iron |
-4.814e-06 |
1.876e-05 |
-0.2566 |
0.801 |
|
| mercury |
-4.576 |
7.18 |
-0.6373 |
0.5335 |
|
| lead |
0.2943 |
0.1237 |
2.379 |
0.03107 |
* |
## FULL MODEL
## Diagnostics: cf plots
## RMSE from cross-validation: 0.454785
Variance Inflation Factors
| VIF |
2.19 |
1.86 |
3.63 |
2.85 |
1.21 |
3.25 |

## REDUCED MODEL
## Adjusted R2 is: 0.21
Fitting linear model: H ~ chromium + lead
| (Intercept) |
1.296 |
0.2619 |
4.949 |
8.918e-05 |
* * * |
| chromium |
-0.02438 |
0.01271 |
-1.918 |
0.07024 |
|
| lead |
0.2364 |
0.09427 |
2.508 |
0.02137 |
* |
## REDUCED MODEL
## Diagnostics: cf plots
## RMSE from cross-validation: 0.4430522
Variance Inflation Factors
| VIF |
2.7 |
2.7 |

Piélou’s evenness
## FULL MODEL
## Adjusted R2 is: -0.23
Fitting linear model: J ~ arsenic + cadmium + chromium + iron + mercury + lead
| (Intercept) |
0.5733 |
0.2064 |
2.778 |
0.01407 |
* |
| arsenic |
-0.006316 |
0.02247 |
-0.2811 |
0.7825 |
|
| cadmium |
0.4514 |
2.013 |
0.2242 |
0.8256 |
|
| chromium |
0.006524 |
0.008932 |
0.7304 |
0.4764 |
|
| iron |
2.023e-06 |
8.981e-06 |
0.2252 |
0.8248 |
|
| mercury |
2.501 |
3.437 |
0.7275 |
0.4781 |
|
| lead |
-0.05506 |
0.05924 |
-0.9295 |
0.3674 |
|
## FULL MODEL
## Diagnostics: cf plots
## RMSE from cross-validation: 0.2719167
Variance Inflation Factors
| VIF |
2.19 |
1.86 |
3.63 |
2.85 |
1.21 |
3.25 |

## REDUCED MODEL
## Adjusted R2 is: 0
Fitting linear model: J ~ 1
| (Intercept) |
0.7955 |
0.04487 |
17.73 |
4.118e-14 |
* * * |
## REDUCED MODEL
## Diagnostics: cf plots
## RMSE from cross-validation: 0.2163708
Quitting from lines 419-420 (C1_analyses_16B.Rmd) Error in Qr$qr[p1, p1, drop = FALSE] : indice hors limites De plus : There were 26 warnings (use warnings() to see them)
5. Multivariate regressions
Independant variables are habitat parameters or heavy metal concentrations, dependant variables are species abundances. Outliers and correlated variables have been excluded from the analysis.
This analysis has been done on PRIMER, with a DistLM to identify the variables that explain the most the community variability and with a dbRDA to plot the results.
Design 1

Variables selected by the DistLM procedure have a \(R^{2}\) of 0.08.
Design 2

Variables selected by the DistLM procedure have a \(R^{2}\) of 0.27.